Nursing Perceptions of Robotic Technology in Healthcare: A Pretest–Posttest Survey Analysis Using an Educational Video

OCCUPATIONAL APPLICATIONS

We used a survey to evaluate the perceptions of nurses and nursing students on robotic technology for nursing care before and after reviewing an educational video that included examples of medical, care, and healthcare service robotic technology. We found that the perception of robotic technology was innately favorable and became more favorable after the video. It is beneficial for engineers to incorporate nurses’ frontline knowledge into the design process from the beginning, while functional changes can be implemented since nurses comprise the largest group of healthcare professionals in hospitals and are the end users of technological devices. Educating nurses in state-of-the-art technology specific to what designers are developing can enable them to provide relevant insight. Designers and engineers can use this insight to create user-friendly, effective technology that improves not only patient care but also nurse job satisfaction.

TECHNICAL ABSTRACT

Background: Interdisciplinary engineering and nursing collaborations have successfully addressed healthcare-related problems; however, findings highlight consistently that nurse input is underutilized in earlier stages of the design process.

Purpose: Our purpose was to capture the differences in perceptions and highlight the insights of nursing students, faculty, and professionals, before and after learning about robotic technology for nursing care.

Methods: A quasi-experimental, pretest–posttest survey was employed using an educational video. The survey related to the perception of three different categories of healthcare robotic technology (medical, care, and healthcare service), as represented by eight different subcategories: surgical; robotic diagnostic systems; companion; assistive; medication delivery and dispensing; cleaning and disinfecting; telepresence and remote monitoring; delivery. Participants rated each subcategory using a Likert-type scale with a 5-point response format with four items: impact, acceptance, environment, and use. Scores were summated to represent the overall construct of perception. Qualitative data were collected in the form of open-ended responses.

Results: Data were collected from 118 participants, with a survey completion rate of 75%. Mean scores were significantly greater for each of the eight robotic technology subcategories after the educational video, supporting that the video influenced a positive perception of healthcare robotic technology. Themes from comments were categorized into (1) positive, mixed, and negative aspects of the research study, as well as improvements and concerns relating to (2) quality of care, (3) nurse work performance, and (4) nursing as a profession.

Conclusion: An educational video enhanced the favorable perception of robotic technology in healthcare. Training nurses on technology fundamentals helped elucidate their potential concerns and identified appropriate applications. It is essential that engineers provide nurses with fundamental knowledge, consistent language, and context about the technology engineers want to develop so nurses can effectively communicate their needs.

Keywords:

• Nurse perception
• robotic technology
• educational video
• pretest-posttest
• quality of care
• design process

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Correction

1. Introduction

1.1. Motivation

Interdisciplinary collaborations are increasingly being used to help solve complex issues related to human health. Engineers and nurses have worked successfully together, resulting in novel devices such as bottles that assess the neurological development of newborns and socks for people with heart failure that track metrics such as swelling, activity, and weight (Bromiker et al., 2016; Oerther & Glasgow, 2022; Penn Nursing, 2023). A recent scoping review on nursing and engineering collaboration by Zhou et al. (2021) found that the most common type of study areas included patient safety, as well as the management of symptoms, health, information systems, and nursing human resources. Nurses played critical roles in these interdisciplinary collaborations, including evaluator, tester, designer, and requirement analyst (Zhou et al., 2021).

While nursing and engineering collaborations in healthcare are on the rise, one issue consistently highlighted is that nursing input is underutilized in these collaborations (Glasgow et al., 2018; Kangasniemi et al., 2019; Zhou et al., 2021). Involving nurses in the design process from the start is crucial, such as during the requirement analysis and design phases (Zhou et al., 2021). Nurse input should be incorporated during conceptual stages, both before engineers decide the value of any new product or system and throughout the entire design process, instead of just a finished product’s testing or evaluation stages (Glasgow et al., 2018). The same applies to robotics, where researchers recommend that nurses are consulted in developing robots, given their first-hand knowledge about their work requirements, and how these requirements might change as care needs change and populations age (Kangasniemi et al., 2019).

A barrier exists between the idealized settings in which these studies are performed (i.e. prototyping in laboratory environments) and the real-world occupational settings where these collaborations are implemented (i.e. hospitals). Thus, to maximize the chances of successful collaborations, researchers should not work exclusively in academic settings but should also form close collaborations with clinicians, regulators, investors, and the business community (Dupont et al., 2021). One way to close the gap between research and the real-world is to implement design-thinking, a human-centered design framework that consists of non-linear, iterative stages. Often the first step is empathy, which allows designers to gain a deeper understanding of the people or population they are designing for by immersing themselves in the community to gain actionable insight (Design Thinking for Health!, n.d.). Designers need to recognize the valuable role nurses can play in the early phases of the design process and understand nursing work to truly understand their needs.

An important practice in the empathetic stage is adopting a beginner’s mindset and starting the design process without assuming preconceived knowledge of the technology of interest. Education can be an avenue to provide the information necessary for any type of collaborator, whether engineering–nursing or academic–occupational, to actively participate. Education can also empower nurses to actively shape the nature of their work. Activities promoting critical and innovative thinking regarding sensitive and safe integration of robotic technology into patient care is encouraged to preserve the nurse–patient relationship and the aspirations of contemporary nursing (McAllister et al., 2021). It is crucial to provide educational opportunities that expose undergraduate and graduate students to the work of other disciplines, including but not limited to engineering, nursing, and computer science, as well as different specialties of these disciplines, such as digital health, co-design, and data science (Booth et al., 2021).

To showcase the value of an empathetic approach, academic–industrial partnerships, and nurse insight, we educated and surveyed professional nurses and nursing students to provide the foundation for future robotic technology collaborations. We conducted a pretest–posttest survey to evaluate the difference in nursing personnel’s perceptions of robotic technology for nursing care before and after viewing a video that familiarized participants with the current state of robotic technology usage in healthcare. Understanding what nurses experience in real-world care situations will lead to understanding relevant issues and the technology needed. By evaluating nurses’ perceptions about the present use of robotics, we aimed to identify key components essential for future technology development. Furthermore, we emphasize the significance of involving nurses in the conceptual stages of the design process to optimize the utilization of their expertise when addressing healthcare challenges through technology.

1.2. Robotic Technology in Healthcare

It is important to specify what is considered a robot, since the definition of the term can vary in the literature. While international agencies have their own requirements for what constitutes a robot, there is no universally accepted definition in international and national standards (IEEE, 2023) and nuances in the language used prevent even these definitions from being consistent. For example, the Institute for Electrical and Electronics Engineers (IEEE) and the International Organization for Standardization (ISO) define the term robot slightly differently (see standards IEEE 1872 and ISO 8373, respectively). Definitions provided by IEEE focus on control and levels of human intervention needed for operation, while the definitions from ISO tend to focus on application and the intended environment where the robot is to be used. Both agencies, however, use the same mental model to characterize how a robot operates: the sense-think-act paradigm. Robots use sensors to collect data from the environment, such as audio, pressure, temperature, light, and distance (sense), make decisions using algorithms based on the information collected (think), and perform actions in the real world by turning the information collected into instructions for behaviors (act). In our study, robotic technology consists of devices and platforms that utilize robots. Robots are defined as autonomous machines that operate using the sense-think-act paradigm, where autonomous is the ability to deal with an environment and perform behaviors or tasks without human intervention for an extended period.

Robotic technology classification is also unstandardized and, in general, has been parsed into multiple domains based on needs, abilities, performance, and technology (Khan & Anwar, 2020). As such, there are a variety of ways that healthcare robotic technology is categorized. They have been categorized by application (i.e. surgery, therapy, rehabilitation, social), the intended population of usage (i.e. pediatric, older adult, doctor, nurse), the environment it is being used in (i.e. hospital, care facility, home), as well as a combination of multiple categories.

In the United States, the Food and Drug Administration (FDA) reviews and clears medical devices, including robotic technology for healthcare. While a formal definition for the term robot is not provided, medical devices are defined under Section 201(h) of the Food, Drug, and Cosmetic Act as a product (e.g. apparatus, machine, implant, in vitro reagent) that does not achieve its purpose via chemical action or metabolization and is intended to affect the structure or function of living beings for usage in the diagnosis, cure, mitigation, treatment, or prevention of diseases and conditions (U.S. Food & Drug Administration, 2022). The FDA assigns medical devices to classes based on the level of control needed for safe and effective usage, risk the device poses to the patient and/or user, and intended use of the device (U.S. Food & Drug Administration, 2020). When introducing robotic technology to the market, the FDA historically clears surgical robots through the premarket notification 510(k) clearance pathway (Haidegger, 2019), typical to nonexempt low-risk and moderate-risk devices. Depending on the level of autonomy the robot is capable of, future medical robotic technology may be classified as high-risk devices that will be subjected to the more stringent Premarket Approval regulatory pathway (Yang et al., 2017).

As public perception, technology, and related language evolve, the definition of what constitutes a robot will be subject to change. Thus, it is important to explicitly define robotic technology in the context of healthcare and how it is being classified to provide a consistent language for potential healthcare professional collaborators, especially those who specialize in different disciplines. In our study, healthcare robotic technology is used by professionals in the healthcare/clinical setting and performs tasks that interact in some capacity with patients, nurses, doctors, and other healthcare professionals. Three major categories were used to discuss healthcare robotic technology, which were adapted from a study commissioned by Business Finland and Future (Frost & Sullivan, 2020). Each major category is based on the application area and is further divided into subcategories that are based on the robot’s purpose (Figure 1).

Figure 1. Robotic technology in healthcare definitions and examples. Three categories of healthcare robotic technology (Medical, Care, and Healthcare service) are represented by eight subcategories: (a) Surgical—da Vinci https://www.intuitive.com, (b) Robotic diagnostic systems—Nasal Swab Collecting Robot https://brainnavi.com, (c) Companion—Aibo https://us.aibo.com, (d) Assistive—EksoNR https://eksobionics.com, (e) Medication delivery and dispensing—E3 https://www.omnicell.com, (f) Cleaning and disinfecting—LightStrike https://xenex.com, (g) Telepresence and remote monitoring—Ava Mobile Telepresence https://www.avarobotics.com, (h) Delivery—Fetch https://fetchrobotics.com.

Medical

Medical robotic technology aids surgery performed by a human operator and can also perform diagnostic tests for healthcare professionals. Surgical robotic technology is mainly used in the operating room, such as laparoscopic knee or hip procedures (Figure 1(a)). These robots aim to enhance the capabilities of the operator, such as providing more accuracy, stabilization, the ability to operate remotely, and the ability to reach inaccessible places. Robotic diagnostic systems perform tasks related to minimally invasive procedures used in clinical diagnostic labs, such as biopsy, swabs, and other tissue sample collection methods (Figure 1(b)).

Care

Care robotic technology directly interacts with people for social and emotional support and to assist with day-to-day tasks. These robots also may increase quality of life by assisting with regaining function due to physical and cognitive impairments. Companion, social, or personal assistant robotic technology directly interact with people for emotional support and to assist with day-to-day tasks (Figure 1(c)). Depending on the individual’s needs, care robotic technology have a wide range of application that include emotional therapy; combating loneliness; exercise; medication and meal reminders; food and water transport; door and drawer manipulation; and picking and placing objects. Assistive robotic technology can increase quality of life by regaining functions due to physical and cognitive impairments, such as older adult or immobile patient assistance and carriers; rehabilitation robots; prosthetics and exoskeletons (Figure 1(d)).

Healthcare Service

Healthcare service robotic technology provides aid to workers by performing non-critical, but necessary, tasks that typically take time away from performing critical tasks and direct patient care. Medication delivery and dispensing performs tasks related to medication organization and access, pill counting, pouch packaging, blister card filling, and vial compounding care (Figure 1(e)). Cleaning and disinfecting robotic technology involve vacuuming and sterilizing the air and the floor in professional settings, typically using filters and UV light (Figure 1(f)). Telepresence and remote monitoring include a tablet interface, cameras, speakers, and a mobile base (Figure 1(g)). Such tasks include providing remote instruction if onsite caregivers need additional help, interacting with isolated and/or contagious individuals, watching people if monitoring is needed, and increasing medical access in areas that are understaffed. Finally, delivery robotic technology performs auxiliary tasks, such as fetching surgical supplies, waste, linen, medication, meals, and tests (Figure 1(h)).

2. Methods

A quasi-experimental, pretest–posttest survey was employed to capture the perceptions of nursing students, faculty, and professional nurses before and after learning about robotic technology. Approval for this research was received by the Institutional Review Boards of the University of Massachusetts Amherst (IRB #3534) and Baystate Health (IRBNet #1967989-2).

2.1. Participants

The inclusion criteria for participants were professional nurses at Baystate Medical Center, an independent academic medical center, as well as faculty members, graduate students, third- and fourth-year undergraduate students, and post-baccalaureate accelerated students in the Elaine Marieb College of Nursing at the University of Massachusetts Amherst. We contacted potential participants via email to participate in our research study using the appropriate listservs: Baystate nurses, college of nursing faculty members, graduate students (PhD, DNP, CNL, MS), third- and fourth-year undergraduate students, and accelerated nursing students. Emails containing a written description of the study and a flyer were sent to each group separately at least once. Other recruitment procedures included having willing faculty members email our study to their students or allow the research team to advertise the study at the beginning of class and flyers. Survey responses were collected between June 2022 and December 2022, and only surveys with 100% completion were included in the analysis.

2.2. Study Design

Nursing and mechanical engineering researchers developed the initial survey and accompanying video. Prior to distribution, the survey was sent to three other faculty researchers (two nursing and one human factors engineer) for pilot testing and content verification. A five-part survey was created using an online survey platform (Qualtrics, Seattle, WA, USA). The survey was administered exclusively in the English language and consisted of the following:

1. Online Survey Consent Form

2. Questions about current knowledge, experience, and perception, pre-video

3. Video about robotic technology in the healthcare setting (∼11mins)

4. Questions about perception, post-video

5. Demographics (no identifying personal information intended)

The survey was anonymous and the “Anonymize responses” function on Qualtrics was used to prevent respondents’ IP address, location data, and contact information from being recorded. After survey completion, participants could receive compensation ($10 electronic Amazon gift card). If participants opted in for compensation, the primary survey was redirected to a separate, secondary survey. The purpose of the secondary survey was so that participants’ answers to our original survey were not linked to any identifying information. The secondary survey collected their name and email address, was only used for compensation purposes, was only accessible once completing the survey, and used the “Prevent Multiple Submissions” Qualtrics function.

The primary survey measured the construct of nursing personnel perceptions of healthcare robotic technology. Using Likert-type scales with a 5-point response format, participants were asked to rate eight different subscales that represented the eight different subcategories of healthcare robots using four Likert items: impact, acceptance, environment, and use (Table 1). These items were based on evidence acquired from a literature review and formal conversations with nursing faculty to elucidate the utility of incorporating robotic technology in the nursing profession. An optional open-response question was also included for each subcategory of robotic technology. Questions were repeated after the video, and the survey concluded with a final, mandatory open-response question asking for information. The order in which the robot and question categories were presented to participants was randomized.

Table 1. Likert items question prompts.

Item	Question	Response format (1–5)
Impact	How would you describe the impact of {robotic technology subcategory} if introduced in clinical areas?	Very detrimental, Slightly detrimental, Neither detrimental nor beneficial, Slightly beneficial, Very beneficial
Acceptance	What level of acceptance do you think {robotic technology subcategory} would have if introduced in clinical workplaces?	Very unwelcomed, Slightly unwelcomed, Neither unwelcomed nor welcomed, Slightly welcomed, Very welcomed
Environment	How conducive is your physical layout of your clinical workplace to having {robotic technology subcategory}?	Very unfavorable, Slightly unfavorable, Neither unfavorable nor favorable, Slightly favorable, Very favorable
Use	How helpful do you think {robotic technology subcategory} will be when doing your job?	Very unhelpful, Slightly unhelpful, Neither unhelpful nor helpful, Slightly helpful, Very helpful
Pre video open response (optional)	Please provide any information you’d like to include for your decision	N/A
Post video open response (optional)	Please provide any information you’d like to include for your decision and comments on ways you think {robotic technology subcategory} should or should not be used.	N/A
End of survey open response (required)	Please provide any information you’d like to include before the end of the survey	N/A

2.3. Analysis

Statistical analysis was performed using RStudio v 2022.7.0.548 (RStudio, PBC, Boston, MA). Recommendations of Warmbrod (2014) were used for reporting and interpreting Likert-type scale scores. Notably, because a Likert-scale is a collection of related statements, the construct must be analyzed as an aggregate number. Respondents with the highest aggregate scores represent those with the most favorable perception of robotic technology in healthcare, and those with the lowest scores correspond to those with the least favorable perception. Cronbach alpha values were calculated to measure the internal consistency of our survey, where a value of .7 or greater is an accepted standard for reliability. The Cronbach alpha coefficients for the Likert-type scale pretest (.93), posttest (.95), and all subscales (.73−.9) fell within the acceptable range (Table A1 in Supplemental Material).

Similar to a study performed by Zrínyi et al. (2022), descriptive statistics were used to analyze data characteristics. Normality was first assessed by the one-sample Kolmogorov–Smirnov test. Since the Likert-type scale used here rendered non-parametric data, the Wilcoxon signed ranks test was used to compare responses of the pre- and post-video responses.

While the present study is not a mixed-methods study, the end of survey open-response question was informally analyzed for thematic content. Comments were analyzed for themes and categories with commonality (Tietze & McBride, 2020) by three research team members using an iterative approach. Each member first determined themes and categories independently. Then, themes and categories were collated and refined. This process repeated until members agreed with the refined list. Comments were not restricted to a single category or theme; thus, some comments were included more than once.

3. Results

3.1. Participants

A total of 118 responses were collected and 88/118 (75%) participants completed the survey to entirety, with a mean completion time of 53 mins (Table 2). The top three professional backgrounds of the participants were registered nurse (53/88, 60%), patient care assistant (11/88, 13%), and no professional background (7/88, 8%). The mean (SD) age was 37.8 (14.5) years; most participants identified as female (79/88, 90%), followed by male (6/88, 7%), and non-binary/third gender (2/88, 2%). Participants worked in a variety of clinical areas, where the top three areas included acute care/critical care (22/88, 16%), medical surgical (21/88, 15%), and adult health (13/88, 9%). Most participants had experience working in healthcare, with only 7/88 (8%) participants having no professional work experience; the mean (SD) number of years worked in healthcare was 13.4 (13.2) years. Regarding education level, 22/88 (22%) and 12/88 (12%) of participants were current undergraduate or graduate students, respectively. The most common degree held was at least a baccalaureate degree (38/88, 39%), followed by master’s degree (13/88, 13%), and associate’s degree (6/88, 6%).

Table 2. Demographic information (n = 88).

Characteristic	Sample count	Characteristic	Sample count
Age		Background
18–29	33 (38%)	Advance practice registered nurse	4 (5%)
30–39	18 (20%)	Certified nurse’s aid	6 (7%)
40–49	17 (19%)	No professional background	7 (8%)
50–59	11 (12%)	Other	7 (8%)
60+	8 (9%)	Patient care assistant	11 (12%)
N/A	1 (1%)	Registered nurse	53 (60%)
Gender		Experience
Female	79 (90%)	<1	14 (16%)
Male	6 (7%)	1–2	6 (7%)
Non-binary/third gender	2 (2%)	3–5	14 (16%)
Prefer not to say	1 (1%)	6–10	8 (9%)
		10–20	15 (17%)
Education		20+	20 (23%)
Associate’s degree	6 (7%)	N/A	11 (12%)
Baccalaureate degree	39 (44%)
Current graduate	12 (14%)	Clinical
Current undergraduate	22 (25%)	Acute care/critical care	22 (25%)
Diploma	1 (1%)	Adult health	13 (15%)
Doctoral degree: DNP	1 (1%)	Anesthesia	3 (3%)
Doctoral degree: PhD	5 (6%)	Cardiology	6 (7%)
Master’s degree	13 (15%)	Community	4 (5%)
		Emergency/trauma	9 (10%)
Place of employment		Family health	6 (7%)
Academia	8 (9%)	Geriatric/gerontology	6 (7%)
Acute care facility	14 (16%)	Home health	5 (6%)
Ambulatory care setting	13 (15%)	Maternal-child health/obstetrics	3 (3%)
Assisted living facility	2 (2%)	Medical surgical	21 (24%)
Federal government hospital	2 (2%)	Neonatal	2 (2%)
Home health	6 (7%)	Oncology	2 (2%)
Industry	2 (2%)	Other	9 (10%)
Investor-owned (for-profit) community hospital	3 (3%)	Palliative care/hospice	3 (3%)
Nongovernment Not-for-Profit Community Hospital	28 (32%)	Pediatrics	5 (6%)
Nursing home/extended care	3 (3%)	Perioperative	4 (5%)
Other	5 (6%)	Psychiatric/mental health/substance abuse	2 (2%)
Physician practice clinic—both PCP and specialty	1 (1%)	Public health	2 (2%)
Physician practice clinic—primary care physician (PCP)	3 (3%)	Rehabilitation	6 (7%)
Physician practice clinic—specialty care	3 (3%)	School health	1 (1%)
School health service	1 (1%)	Women’s health	6 (7%)
School of nursing	9 (10%)
State/local government community hospital	5 (6%)

3.2. Quantitative Results for Perceptions of Robotic Technology

Participants rated eight different subcategories of healthcare robotic technology using a 5-point response-type Likert-scale that measured the overall construct of nurse perception of robotic technology in healthcare. Descriptive statistics and frequency of responses for each subscale and Likert item are shown in Table A2 in Supplemental Material. Comparing the pre- and post-video results showed statistically significant differences, where the summated scores significantly increased after the video for each robot subcategory (Table 3).

Table 3. Descriptive statistics and pre-post-video comparison of nursing perceptions of robotic technology in healthcare (Wilcoxon test for paired samples).

	Means (SD)		Median		Mode		Wilcoxon
Robot	Pre-video	Post-video	Pre-video	Post-video	Pre-video	Post-video	p value
Surgical	14.8 (3.7)	16.2 (3.2)	15.5	16	16	16	<0.001
Diagnostic	14.3 (3.7)	15.8 (3.5)	15	16	16	16	<0.001
Dispensing	15.3 (3.6)	16.2 (3.7)	16	16	20	20	0.002
Cleaning	16.8 (3.0)	18.0 (2.7)	17	19	20	20	<0.001
Telepresence	14.8 (3.9)	16.9 (3.3)	15	17	15, 20	20	<0.001
Delivery	15.1 (3.6)	16.7 (3.0)	16	17	16	20	<0.001
Companion	13.2 (4.4)	16.1 (3.8)	14	16	16, 20	20	<0.001
Assistive	14.9 (3.5)	16.4 (3.0)	15	16	15, 18, 19	20	<0.001

3.3. End of Survey Comment Categorization

Many participants had additional information to provide when asked to answer the open-ended question, “Please provide any information you’d like to include before the end of the survey” (52/88, 59%). Categories included the research study itself, quality of care, work performance, and nursing as a profession and in the healthcare system (Table 4).

Table 4. Themes categories for the end of survey, open-ended exit question.

Themes category	Themes sub-category	Themes	Frequency (n)	Representative quote
Research study	Positive	Good, interesting, and/or important	9	Overall a great and innovative idea to help the healthcare facilities flourish.
Research study	Positive	Educational	11	I wasn’t too sure about robotics in the healthcare setting due to not having any background knowledge or experiences. After watching the video, it was very informative and insightful. I think the idea is innovative and can also provide the warm/comforting companionship as well as reducing any medical errors.
Research study	Mixed	Categories are confusing but collecting opinions of practicing and student nurses is important	1	I was a little confused when answering the questions vs. the video. The video broke the technology into 4 different categories, so it was a little hard keeping track of which one [robotic technology category] I was answering for in the questionnaire. Otherwise, I really appreciate that this was focused on collecting the opinions of nurses/future nurses. So important!
Research study	Negative	Technical difficulties	1	[T]he video was very hard to hear.
Quality of Care	Improvement	Efficiency and safety	5	I think there are a lot of things to consider with using robotic technology. I do not think they should replace the jobs of anyone, but make it be clear that they are to aid and make the jobs of healthcare workers more efficient. There are just somethings that us, humans must do on our own.
Quality of care	Improvement	Patient satisfaction	2	In my opinion it is very welcome to use robots in nursing area, help patients to have better feeling in their life also it will give them better feeling about hospitalization and so on.
Quality of care	Concerns	Error and safety	5	I believe this technology has a place in healthcare, but my particular perception is based exclusively in the population I care for. My experience with mobile robots has been that they are large, cumbersome and slow moving, which is the direct antithesis of a nurse who needs to be nimble, quicky moving and quick thinking. My fear is that a robot would be between me and my patient, causing a delay in emergent care.
Work performance	Improvement	Workload and workflow	3	With technology becoming increasingly involved in everyday life, it can be beneficial to see more technological involvement in healthcare. Tedious, small tasks can easily be fulfilled by robots which can allow nurses and other healthcare workers to better prioritize their mandated tasks to provide the best care possible.
Work performance	Improvement	Short staffing	1	Robotic technology is the wave of the future. With short staff at most hospitals the robots will be in more demand. [sic]
Work performance	Improvement	Burnout	1	This is a necessary technology to reduce nursing burnout
Work performance	Improvement	Specific robotic technology	5	I have worked in facilities that use robots for late food tray delivery and that works well. Also, the VNA in the area I work, uses a telemonitoring system for CHF patients that works very well. The [da Vinci] is used by many surgeons at the hospital I work at. I don’t have personal experience with it. It would not offer much improvement for my job. I don’t see how the med robot would help in hospitals- seems better for outpatient pharmacies or in settings where patients don’t need a lot of supervision/monitoring.
Work performance	Concerns	Job security	4	I think the use of robots is a great help. I do feel the “older” generation of nurses will perceive robots as a way of taking away their jobs/skills
Work performance	Concerns	Personal touch and empathy	6	I found this survey and the education about robotics use in healthcare intriguing. I think there is a place for robotics in health care and their use could lead to improved accuracy, efficiency and quality of patient care, but there is still work to be done to define the appropriate devices within settings. I think it is important to understand the patient’s perspective on use of robotics. I still believe that human touch is part of the art and science of healing and feeling cared for.
Work performance	Concerns	Specific robotic technology	3	I feel as though the robotic companions would negatively impact patients, as they are unable to provide empathy/emotions that patients often need
Nursing in the healthcare system	Improvement	Specific workplace	5	I would be interested in learning more about robotic devices and how they could assist in the ICU setting
Nursing in the healthcare system	Improvement	Nondescriptive	5	I think all in all it will be the most beneficial. It will be hard to sway some people due to their [sic] lack of knowledge when it comes to technology, the possibility of malfunctioning, and the possibility of decreasing job opportunities. However, the beauty of technology can do outstanding things within the healthcare field, it just has to gain the trust of the people.
Nursing in the healthcare system	Concerns	Adoption and implementation	3	very informative video. I do think that the current culture in healthcare may be resistant in adopting new technology such as robotics, in regards to performing such things as testing(nasal swabs…) Although helpful, it reduces facetime between provider and patient.
Nursing in the healthcare system	Concerns	Appropriate technology selection	4	Would be curious how much time it takes to get the robots working and the person who needs to service them compared to an actual person doing the job.
Nursing in the healthcare system	Concerns	Funding and accessibility	1	I think in theory robotic technology would be great but most of the hospitals or health care facilities that would benefit from this are places that can fund such technology. If this technology can be used in smaller, low-income healthcare facilities, that is where it would be most beneficial.

Research Study

Most comments supported the purpose of the study, and one of the main themes was that the study was good, important, or interesting. Equally frequent, the other main theme was related to the educational aspect of the study, where participants found the topic in general or the video showcasing current robotic technology used in healthcare to be informative. Of the remaining comments about the research study, one participant was both positive and negative, stating it was hard to keep track of the categorization of robotic technology when answering the questions, but “… otherwise, I really appreciate that this was focused on collecting the opinions of nurses/future nurses. So important!” Additionally, only one participant critiqued a technical aspect of the video, as “the video was very hard to hear.”

Quality of Care

The most prominent potential benefit robotic technology can provide for quality of care is efficiency and safety. Participants mentioned reducing medical errors or improving accuracy, providing a safer environment, improving efficiency when treating patients, and improving the quality of care in general. Notably, error and safety were the top concerns for quality of care, where the possibility of malfunctioning and errors in general, causing physical harm to patients due to miscalculations, double-checking for medication mistakes, and causing delays in emergent care were mentioned. While infrequent, patient satisfaction was identified as a potential area robotic technology could improve by providing warmth and comfort, as well as helping patients feel better about their life and hospitalization.

Work Performance

Nurse work performance was another reoccurring category. Specific potential benefits were scarce but included workload or workflow, where participants highlighted lessening the workload in general, improving nurse workflow, and prioritizing tasks. Other areas robotic technology could improve work performance by supporting short staffing and reducing burnout. Specific examples of robotic technology were mentioned for their potential benefits, including PARO the seal, food tray delivery, telemonitoring for congestive heart failure patients, medication robots for outpatient pharmacies or settings that do not involve supervision/monitoring, cleaning, and disinfecting. However, concerns for specific robotic technology were also prevalent where participants highlighted the potential negative outcomes related to physical aspects of robotic nasal swabs and emotional aspects of robotic companions. The most common concern related to Nurse Work Performance was the personal touch and empathy, stating that there are certain tasks that humans must do, lack of human interaction can be scary, robotic technology is unable to provide empathy and emotions that patients often need, robotic technology can reduce facetime between provider and patient, and that it is important to understand the patient’s perspective. Finally, job security was identified as a potential consequence of robotic technology incorporation, including the replacement of skills and decreased job opportunities.

Nursing as a Profession and in the Healthcare System

The final category was the implications robotic technology would have on nursing as a profession or in the context of the entire healthcare system. Themes were evenly split between specific workplaces (Intensive care unit, operating room, small low-income health facilities, and office settings) and nondescriptive improvements. Comments categorized as nondescriptive improvements described robotic technology in a positive manner but did not specify details as to how the nursing profession or healthcare system would improve. Concerns for robotic technology and the nursing profession highlighted defining the appropriate technology selection, adoption and implementation, and funding and accessibility.

4. Discussion

Nurses comprise the largest group of healthcare professionals in hospitals worldwide, interact with patients more frequently than any other member of the clinical care team, and play an integral role for communication as the interface between patients, visitors, and other healthcare professionals (Kriegel et al., 2021). Combined with being the direct end-users of healthcare technology, nurses have valuable insight, experience, and potential to be innovators and evaluators (McAllister et al., 2021). It is crucial that engineers understand nurse perceptions of current technology and how these perceptions may vary as a function of robot type (Mitzner et al., 2018). To do so, engineers must provide education on the technology of interest so nurses can effectively communicate relevant insight. Relevant insight is valuable and has potential to mitigate the negative and enhance the positive impacts of technology on professional autonomy, patient relationships, routines, and workflow. In this study, nursing personnel perceptions of and insight into robotic technology were explored before and after watching an educational video depicting the current state of robotic technology in healthcare.

4.1. Educational Video

In our study, an interdisciplinary team of engineering and nursing researchers worked together to create an educational video and survey that provided practicing and student nurses an interprofessional education opportunity to familiarize themselves with current robotic technology in healthcare. The interdisciplinary co-creation of the video and survey was informative and interesting, as evidenced by the end of the survey comments and 75% completion rate despite the average completion time of 53 mins. Insight, such as that provided by the end of survey comments, may influence how robotic technology can and should be designed and utilized by nurses to assist patient care, which in turn can help engineers create ergonomic and effective devices.

Participants positively described our study at the end of the survey and thought the educational video was informative. Videos have been successfully used as general educational resources as well as interventions in robotics. Zrínyi et al. (2022) explored the usefulness of using a video intervention approach in changing the perception of student nurses. In their quasi-experimental, before-and-after experiment, participants watched three videos showing a social robot (Sophia, Hanson Robotics), geriatric companion (Pepper, SoftBank Robotics), and blood sampling robot (Veebot, Veebot Systems Inc.). The authors noted that the main effect of the intervention was an increase in the general liking and acceptance of care robots since all dimensions of robot agreement improved after the intervention. Similarly, our study demonstrated that a visual approach is useful as an educational tool and when characterizing perceptions.

Prior research has evaluated barriers to adopting robotic technology, which include the culture in which nurses practice (Papadopoulos & Koulouglioti, 2018), as well as the negative feelings and emotions toward the inclusion of robots in nursing care (Papadopoulos et al., 2020; Zrínyi et al., 2022). Educational interventions have been successful where pilot studies have shown that hands-on interactions (Broadbent et al., 2010; Saadatzi et al., 2020) and video interventions (Lee et al., 2020; Zrínyi et al., 2022) elicited positive effects such as increased endorsement of the robot. Our study supports that feelings toward robots become more positive and less negative with exposure. The video defined eight subcategories of healthcare robotic technology, including over 30 different robots as examples. The perceptions of robotic technology became significantly more positive after the participants were exposed to different examples of robots, highlighting that education may be one way to surmount adoption barriers engineers may face when implementing robotic technology in real, occupational settings.

4.2. Human Connection and the Role of Companion Robots

Personal touch and empathy were identified as subcategories related to Work Performance. While two participants mentioned that robotic technology can provide warmth, companionship, and improve patients’ feelings regarding their hospitalization, one participant said it would negatively impact patients due to an inability to provide empathy and emotions that meet patients’ needs. This conflicting view is consistent with previous research; Liang et al. (2019) conducted semi-structured interviews with nursing staff to describe their views on the potential use of robots in the pediatric unit. Participants felt robots could respond to and provide a reassuring presence for children when care is needed, but nurses are not immediately available. In contrast, participants also described robots as unable to discern patients’ needs, culture, or provide individualized responses.

Participant comments showcased the duality of sentiment in robotic technology’s emotional impact on patients, and this mimics the contrasting nature of studies using companion robots. Experimental data for the role of robotic technology in providing companionship and emotional therapy, while promising for aiding specific populations, needs work to leverage technology to improve patient outcomes and reduce workload. Socially assistive robotics (SARs) in therapy, which our study refers to as care robotic technology, aim to engage in social interactions with humans and assist people. SARs can increase social engagement for children with autism spectrum disorder (Coeckelbergh et al., 2016), where robots have improved the ability to identify sadness and happiness (Pop et al., 2013) and have helped increase body awareness and appropriate physical interaction (Costa et al., 2015). Additionally, using hormonal markers, stress decreases for older adults when using robot therapy (Kanamori et al., 2002; Shibata & Wada, 2011).

Unfortunately, many studies involving SAR technology in older adult care had methodological issues (Abdi et al., 2018; Bemelmans et al., 2012; Broekens et al., 2009; Yu et al., 2022). For example, a recent review investigating robotic technology support for dementia care identified specific types of robotic technology that were considered acceptable; however, while people with dementia, family caregivers, and staff enjoyed using robots, inconsistent evidence about the feasibility and inconclusive evidence that cognition, neuropsychiatric symptoms, or quality of life was improved potentially prevents widespread adoption (Yu et al., 2022). The authors concluded that a lack of evidence is not evidence of a lack of effectiveness and recommend future research use high-quality designs. Regardless, robotic technology currently cannot fully replace the significance of human touch, genuine empathy, and intimate personal relationships (McAllister et al., 2021). These elements are critical in providing care for vulnerable populations, such as companionship, and handling sensitive information through telepresence and remote monitoring. Moreover, numerous ethical and data security concerns must be considered (Glasgow et al., 2018).

4.3. Occupational Ergonomics and Human Factors

Our qualitative results supported the occupational applications of human-robot collaboration within the healthcare field. The subcategory of Quality of Care described how robotic technology would impact nursing from the viewpoint of patient health outcomes and patient safety. The Nurse Work Performance subcategory connects to how robotic technology would impact the nurse individually. Finally, nursing as a profession was an occupation-specific category from the viewpoint of nursing as a collective profession and in relation to the healthcare field in general. This suggests that nursing personnel are aware of robotic technology’s potential benefits and limitations in relation to human-system performance and human health and safety.

Regarding occupational ergonomics in relation to Quality of Care, error, safety, and efficiency were categories identified at the end of survey comments. When considering human factors, errors are usually a consequence of incompatibilities between humans, systems, and technology (Mao et al., 2015). The cost associated with medication errors, which includes errors in prescribing, preparing/dispensing, administering, and monitoring, has been estimated at $42 billion USD worldwide (Aitken & Gorokhovich, 2012). In an integrated review identifying how automated devices are currently used, the largest existing field for nursing automated devices at care institutions was medication care, and the most frequently described outcomes were medication errors (Kangasniemi et al., 2019). Automated dispensing cabinets (ADCs), like those that provide safeguards for nurses to remove the correct medications, can reduce medication errors with prescription and dispensing errors (Tu et al., 2023). Aside from medication error reduction, ADCs have also been found to make work easier for ICU nurses, and observational studies in the OR showed that the time spent on dispensing and preparing medications decreased by an average of half an hour per 8-hr shift (Metsämuuronen et al., 2020). While efficiency is important, there is concern for the potential of dehumanized and depersonalized workplaces that favor automation and efficiency over personal connection (McAllister et al., 2021). Policies to protect nurses must accompany the initiatives to automate, ensuring that time gained from offloading tasks is used for improving nursing care, patient education, and patient interaction instead of being misused for more patients (Tietze & McBride, 2020).

Regarding Nurse Work Performance, our participants highlighted lessening the workload, improving nurse workflow, mitigating short staffing, and reducing burnout. One participant stated that robotic technology can fulfill tedious, small tasks, allowing nurses to prioritize mandated tasks better. Previous studies have reached similar conclusions about how robotic technology can improve nursing work by automating routine tasks (Blechar & Zalewska, 2020). Nurses welcome robotic technology that reduces their workload and workflow, specifically nursing activities that do not directly affect patient outcomes or may even compromise nursing productivity (Lee et al., 2018). Thus, one recommendation is that robotic technology performs routine nursing care tasks dictated solely by highly defined, prescribed procedures and accomplishments (Pepito & Locsin, 2019).

Notably, concerns associated with workload reduction included reducing the flexibility and customization of individualized nursing service, looking after robots operated near patients, mechanical failures, inappropriate care provided, and the need for user-friendly devices that do not increase workload (Lee et al., 2018). A crucial concern related to workload improvements was decreasing job and skill opportunities. Participants responded about job replacement, decreased opportunities, and taking away skills. The fear of being replaced by robotic technology has been a common sentiment (Maalouf et al., 2018; Pepito & Locsin, 2019). Respondents had even expressed contradictory feelings where they understood the intended role of robots was to assist nursing staff, particularly with precise and repetitive work; yet, participants worried robots would eventually replace nursing staff, resulting in job loss (Liang et al., 2019). Robotic technology can facilitate or improve the nursing process but cannot replace nurses who provide holistic care (Blechar & Zalewska, 2020). Furthermore, considering the global shortage of nurses, the aim is for robots to complement nursing tasks rather than replace them, enabling nurses to dedicate more time to activities that increase the patient’s quality of care and improve their job satisfaction.

Nurses, as representatives of the largest profession in the healthcare area, are in a unique position to provide vital information related to technological advances. Having nurses at the table to direct the conversation as it relates to the collaboration of technological developments is an important next step to ensure that such developments align with the standards of ethics of nursing practice. One caveat with this approach is that it is of the utmost importance also to consider the impact of asking nurses to be technology innovators. This could be seen as a potential burden due to adding yet another responsibility for nurses and those involving robotics competency. Regardless, nurses’ efforts to collaborate have the potential to streamline health care delivery and improve health outcomes. The findings from this study were a first step in understanding how many nurses perceive the use of robotic technology specifically as it relates to the advancement of the profession. As mentioned earlier, many nurses remarked on the excitement factor that exists in welcoming robotic technology into healthcare. Some nurses expressed a more skeptical viewpoint. Future studies must further elucidate the specific factors that influence nurse skepticism and those factors that elicit more excitement.

4.4. Limitations and Future Recommendations

Since randomization was not met, and due to the limited sample size, the results of this study may not be applicable to other nursing groups that do not share similar demographic characteristics. Further, this study has limitations that are inherent to pretest-posttest study design, such as the tendency of a group to move to a common mean as an artifact of repeated testing (statistical regression); changes in retaining information or skills that occur with time (knowledge or attitude “decay”); and the loss of positive results of an intervention over time due to the influence of personal experience and external stimulants that may cause attitudes to change rapidly (Stratton, 2019).

The educational video we used may impact the generalizability of the findings. The content for the video, including visuals and accompanying narration, only discussed and showcased robotic technology that worked as intended and in a controlled environment. The sample videos’ original purpose was to display the capabilities of the robot, and they were often promotional videos created by the company. These videos showed best-case scenarios with no malfunctions or delays and did not show the consequences of misuse or errors. Future work must include a more realistic representation of how technology would be used in daily life.

Since the qualitative feedback provided at the survey’s end was insightful, we suggest future studies utilize focus groups, interviews, or observations. These methods collect a wide variety of data and can be utilized during any step of the design process. Data collected can be used for needs assessments, planning, and pilot testing when implemented before; for formative evaluation, reporting, and monitoring when used during; and for summative evaluation, outcome evaluation, and feedback when used after performing a study (Krueger & Casey, 2015). For our purposes, focus groups and interviews can be used to have in-depth discussions on technology concepts that have potential for development, specifically elucidating the factors that influence both skepticism and excitement. Observational studies, on the other hand, gather data regarding activity conducted in complex systems, such as nursing, and provide an excellent opportunity for designers to gain empathy and a deeper understanding of their clients.

Furthermore, we recommend that designers allow nurses to clearly define the role of the robotic technology intended to assist them and that those responsible for its integration into the healthcare setting ensure this role is not overstepped. Incorporating technology into the healthcare system is ongoing and will change nursing practice. The nursing profession must consider its role, knowledge, and relationships with technologies and patients to continue to provide compassionate care in digitally enabled societies and healthcare systems (Booth et al., 2021). Collaboration with industry, academia, healthcare providers, and patients will be essential to create appropriate robotic technology that supports nurse-defined models of care and approaches.

4.5. Conclusion

Our results show that an educational video enhanced the perception of robotic technology in healthcare and the importance of interdisciplinary collaboration at the beginning of the design process. When bringing robotic technology to clinical use, a genuine need must be identified, and the relevant technology developed that addresses this need must consider the value the robot adds to the clinician and patient (Dupont et al., 2021). Frontline nursing knowledge must be incorporated throughout the design process to implement functional changes to create user-friendly, effective technology that improves patient care and nurse job fulfillment. Nurse perception of technology within clinical settings should be utilized to (a) identify relevant clinical needs and the appropriate technology required to meet these needs and (b) identify ergonomic improvements in existing technology to improve known problems. In conclusion, our study used an educational video to increase perception and gather valuable insights from nurses and nursing students, providing a foundation for identifying the opportunities for incorporating robotic technology within the healthcare setting.

Acknowledgments

The authors wish to thank Dr. Joohyun Chung, Dr. Karen Giuliano, and Dr. Shannon C. Roberts for their assistance in pilot testing our survey and providing feedback.

Conflict of interest

The authors declare no conflict of interest.

Additional information

Funding

This research was supported by the Elaine Marieb Center for Nursing and Engineering Innovation. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the Elaine Marieb Center for Nursing and Engineering Innovation.